Classification of cyber-physical production systems applications: Proposition of an analysis framework

Cyber-physical systems have encountered a huge success in the past decade in several scientific communities, and specifically in production topics. The main attraction of the concept relies in the fact that it encompasses many scientific topics that were distinct before. The downside is the lack of readability of the current developments about cyber-physical production systems (CPPS). Indeed, the large scientific area of CPPS makes it difficult to identify clearly and rapidly, in the various applications that were made of CPPS, what are the choices, best practices and methodology that are suggested and that could be used for a new application. This work intends to introduce an analysis framework able to classify those developments. An extensive study of literature enabled to extract the major criteria that are to be used in the framework, namely: Development Extent; Research Axis; Instrumenting; Communication standards; Intelligence deposit; Cognition level; Human factor. Several recent examples of CPPS developments in literature are used to illustrate the use of the framework and brief conclusions are drawn from the comparative analysis of those examples.

💡 Research Summary

The paper addresses the growing difficulty of navigating the rapidly expanding literature on Cyber‑Physical Production Systems (CPPS). While CPPS promises integrated, autonomous, and human‑centered manufacturing, the sheer breadth of applications and definitions hampers researchers and engineers from quickly identifying best practices, methodological choices, and technology trends. To remedy this, the authors propose an analysis framework that classifies CPPS developments along seven key criteria, derived from an extensive literature review and from the three fundamental capabilities of Cyber‑Physical Systems (communication, computation, control).

The framework’s first dimension is the Development Extent, which captures the maturity level of a CPPS project: (1) Laboratory Experiments (Lab XP), (2) Proof‑of‑Concept (POC), (3) Industry‑scale deployment, and (4) Learning Factory. This hierarchy enables a quick assessment of whether a work is at a proof‑of‑principle stage or ready for real‑world production.

The second dimension, Research Axis, aligns each CPPS case with the major strategic goals identified in international roadmaps (e.g., advanced technologies, sustainability, human‑centered production, and system agility). By mapping a project to one or more of these axes, the framework situates it within the broader policy and research agenda.

The remaining five dimensions focus on technical and human‑interaction aspects:

• Communication Standards – which protocols and network technologies (e.g., OPC‑UA, MQTT, 5G, fog computing) are used to interconnect sensors, actuators, and higher‑level services.
• Intelligence Deposit – where the decision‑making logic resides (central cloud, edge devices, distributed agents).
• Cognitive Abilities – the level of knowledge management, learning, and autonomous adaptation employed (rule‑based, model‑based, AI‑driven).
• Instrumentation – the degree of pervasive sensing, ranging from basic PLC inputs to advanced digital twins and auto‑identification technologies.
• Human‑Machine Interface – how operators interact with the system, covering collaborative robots, AR/VR training, and ergonomic workstation design.

To demonstrate the framework’s utility, the authors apply it to several recent CPPS case studies drawn from the literature. For each case they populate the seven criteria, revealing patterns such as: mature industrial deployments tend to use standardized high‑speed communication (OPC‑UA/5G), edge‑based intelligence, real‑time optimization algorithms, and sophisticated human‑machine interfaces, whereas proof‑of‑concept prototypes often rely on laboratory‑scale PLC communication, cloud‑centric intelligence, and limited cognitive functions.

The comparative analysis shows that most contemporary CPPS research converges on the “agility” axis—emphasizing reconfigurability, distributed intelligence, and mass customization—while simultaneously addressing sustainability and human‑centered concerns. The framework also highlights gaps; for instance, many studies under‑represent the socio‑psychological aspects of human interaction, treating the “human factor” as a single checklist item.

The authors acknowledge limitations: the criteria are partly qualitative, and the inter‑dependencies among them can introduce subjectivity when classifying complex systems. They propose future work to assign quantitative weights, develop a searchable CPPS case database, and create automated tools that map new projects onto the framework.

In conclusion, this paper delivers a structured, roadmap‑aligned taxonomy that transforms the fragmented CPPS literature into an accessible map. By providing a clear set of classification axes, the framework assists researchers in rapidly locating relevant prior work, guides engineers in selecting appropriate technologies for each development stage, and supports policymakers in tracking progress toward strategic manufacturing objectives.